The aim of the present paper is to provide a statistical test for matched pairs with missing data which does not require any parametric assumptions and uses all observations available. We propose new maximum mean discrepancy (MMD) estimators to achieve this aim [1]}. The energy distance and the MMD are two equivalent statistical metrics with the ability to detect distributional differences between random samples [2]}, [3]}. Moreover, MMD-based statistics can also be seen as a natural generalization of the ANOVA test to cases where the distributions are not necessary Gaussian [4]}. MMD overcomes Gaussian assumptions by representing distances between distributions as distances between mean embeddings in a reproducing kernel Hilbert space (RKHS). MMD has been successfully applied to independence testing [5]}, two-sample testing [1]}, survival analysis [7]}, or clustering analysis [8]}.
